



'''simple docstring'''

import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_PRETRAINING_MAPPING,
			    NezhaForMaskedLM,
			    NezhaForMultipleChoice,
			    NezhaForNextSentencePrediction,
			    NezhaForPreTraining,
			    NezhaForQuestionAnswering,
			    NezhaForSequenceClassification,
			    NezhaForTokenClassification,
			    NezhaModel,
			)
			from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST

class 							a__:






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=7					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=99					,   _UpperCAmelCase=32					,   _UpperCAmelCase=5					,   _UpperCAmelCase=4					,   _UpperCAmelCase=37					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=128					,   _UpperCAmelCase=32					,   _UpperCAmelCase=16					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=3					,   _UpperCAmelCase=4					,   _UpperCAmelCase=None					,   )							->							Tuple:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =seq_length
					snake_case__	     =is_training
					snake_case__	     =use_input_mask
					snake_case__	     =use_token_type_ids
					snake_case__	     =use_labels
					snake_case__	     =vocab_size
					snake_case__	     =hidden_size
					snake_case__	     =num_hidden_layers
					snake_case__	     =num_attention_heads
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =max_position_embeddings
					snake_case__	     =type_vocab_size
					snake_case__	     =type_sequence_label_size
					snake_case__	     =initializer_range
					snake_case__	     =num_labels
					snake_case__	     =num_choices
					snake_case__	     =scope






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size						)

					snake_case__	     =None
					if self.use_input_mask:
							snake_case__	     =random_attention_mask([self.batch_size, self.seq_length]						)

					snake_case__	     =None
					if self.use_token_type_ids:
							snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size						)

					snake_case__	     =None
					snake_case__	     =None
					snake_case__	     =None
					if self.use_labels:
							snake_case__	     =ids_tensor([self.batch_size]					,   self.type_sequence_label_size						)
							snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels						)
							snake_case__	     =ids_tensor([self.batch_size]					,   self.num_choices						)

					snake_case__	     =self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






			def 						_lowercase  (     self						)							->							Any:
					return NezhaConfig(
					    vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_UpperCAmelCase					,   initializer_range=self.initializer_range					,   )






			def 						_lowercase  (     self						)							->							Tuple:
					(
					    (
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	
					)	     =self.prepare_config_and_inputs()

					snake_case__	     =True
					snake_case__	     =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2						)

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    input_mask,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =NezhaModel(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase						)
					snake_case__	     =model(_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase						)
					snake_case__	     =model(_UpperCAmelCase						)
					self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)						)
					self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.hidden_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							Dict:
					snake_case__	     =True
					snake_case__	     =NezhaModel(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   encoder_hidden_states=_UpperCAmelCase					,   encoder_attention_mask=_UpperCAmelCase					,   )
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   encoder_hidden_states=_UpperCAmelCase					,   )
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase						)
					self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)						)
					self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.hidden_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Tuple:
					snake_case__	     =NezhaForMaskedLM(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							List[str]:
					snake_case__	     =NezhaForNextSentencePrediction(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase					,   )
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, 2)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =NezhaForPreTraining(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase					,   next_sentence_label=_UpperCAmelCase					,   )
					self.parent.assertEqual(result.prediction_logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)						)
					self.parent.assertEqual(result.seq_relationship_logits.shape					,   (self.batch_size, 2)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =NezhaForQuestionAnswering(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   start_positions=_UpperCAmelCase					,   end_positions=_UpperCAmelCase					,   )
					self.parent.assertEqual(result.start_logits.shape					,   (self.batch_size, self.seq_length)						)
					self.parent.assertEqual(result.end_logits.shape					,   (self.batch_size, self.seq_length)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =self.num_labels
					snake_case__	     =NezhaForSequenceClassification(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =self.num_labels
					snake_case__	     =NezhaForTokenClassification(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =self.num_choices
					snake_case__	     =NezhaForMultipleChoice(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =input_ids.unsqueeze(1						).expand(-1					,   self.num_choices					,   -1						).contiguous()
					snake_case__	     =token_type_ids.unsqueeze(1						).expand(-1					,   self.num_choices					,   -1						).contiguous()
					snake_case__	     =input_mask.unsqueeze(1						).expand(-1					,   self.num_choices					,   -1						).contiguous()
					snake_case__	     =model(
					    _UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   token_type_ids=_UpperCAmelCase					,   labels=_UpperCAmelCase					,   )
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_choices)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.prepare_config_and_inputs()
					(
					    (
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	
					)	     =config_and_inputs
					snake_case__	     ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
					return config, inputs_dict





@require_torch
class 							a__( snake_case__   ,      snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      (
			    (
			        NezhaModel,
			        NezhaForMaskedLM,
			        NezhaForMultipleChoice,
			        NezhaForNextSentencePrediction,
			        NezhaForPreTraining,
			        NezhaForQuestionAnswering,
			        NezhaForSequenceClassification,
			        NezhaForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			a_	: Optional[Any]									=      (
			    {
			        '''feature-extraction''': NezhaModel,
			        '''fill-mask''': NezhaForMaskedLM,
			        '''question-answering''': NezhaForQuestionAnswering,
			        '''text-classification''': NezhaForSequenceClassification,
			        '''token-classification''': NezhaForTokenClassification,
			        '''zero-shot''': NezhaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			a_	: Tuple									=      True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=False						)							->							Tuple:
					snake_case__	     =super()._prepare_for_class(_UpperCAmelCase					,   _UpperCAmelCase					,   return_labels=_UpperCAmelCase						)

					if return_labels:
							if model_class in get_values(_UpperCAmelCase						):
									snake_case__	     =torch.zeros(
									    (self.model_tester.batch_size, self.model_tester.seq_length)					,   dtype=torch.long					,   device=_UpperCAmelCase						)
									snake_case__	     =torch.zeros(
									    self.model_tester.batch_size					,   dtype=torch.long					,   device=_UpperCAmelCase						)
					return inputs_dict






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =NezhaModelTester(self						)
					snake_case__	     =ConfigTester(self					,   config_class=_UpperCAmelCase					,   hidden_size=37						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					self.config_tester.run_common_tests()






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							int:
					# This regression test was failing with PyTorch < 1.3
					(
					    (
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	(
					    snake_case__
					)     ,	
					)	     =self.model_tester.prepare_config_and_inputs_for_decoder()

					snake_case__	     =None

					self.model_tester.create_and_check_model_as_decoder(
					    _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_next_sequence_prediction(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[int]:
					for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =NezhaModel.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)






			@slow
			@require_torch_gpu
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							# NezhaForMultipleChoice behaves incorrectly in JIT environments.
							if model_class == NezhaForMultipleChoice:
									return

							snake_case__	     =True
							snake_case__	     =model_class(config=_UpperCAmelCase						)

							snake_case__	     =self._prepare_for_class(_UpperCAmelCase					,   _UpperCAmelCase						)
							snake_case__	     =torch.jit.trace(
							    _UpperCAmelCase					,   (inputs_dict['input_ids'].to('cpu'						), inputs_dict['attention_mask'].to('cpu'						))						)

							with tempfile.TemporaryDirectory() as tmp:
									torch.jit.save(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   'bert.pt'						)						)
									snake_case__	     =torch.jit.load(os.path.join(_UpperCAmelCase					,   'bert.pt'						)					,   map_location=_UpperCAmelCase						)
									loaded(inputs_dict['input_ids'].to(_UpperCAmelCase						)					,   inputs_dict['attention_mask'].to(_UpperCAmelCase						)						)





@require_torch
class 							a__( unittest.TestCase     ):






			@slow
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =NezhaModel.from_pretrained('sijunhe/nezha-cn-base'						)
					snake_case__	     =torch.tensor([[0, 1, 2, 3, 4, 5]]						)
					snake_case__	     =torch.tensor([[0, 1, 1, 1, 1, 1]]						)
					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase						)[0]
					snake_case__	     =torch.Size((1, 6, 768)						)
					self.assertEqual(output.shape					,   _UpperCAmelCase						)
					snake_case__	     =torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]						)

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,   _UpperCAmelCase					,   atol=1E-4						)						)






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'						)
					snake_case__	     =torch.tensor([[0, 1, 2, 3, 4, 5]]						)
					snake_case__	     =torch.tensor([[1, 1, 1, 1, 1, 1]]						)
					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase						)[0]
					snake_case__	     =torch.Size((1, 6, 2_1128)						)
					self.assertEqual(output.shape					,   _UpperCAmelCase						)
					snake_case__	     =torch.tensor(
					    [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]						)

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,   _UpperCAmelCase					,   atol=1E-4						)						)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




